The invention can be used in navigation-assisted operations and can be adduced for planning operative incisions. Briefly outlined: in navigation-assisted operations, the current position of a patient or part of a patient's body on the one hand, and the position of medical treatment apparatus such as for example a scalpel on the other, is detected by a detection unit, and the detected position data are assigned to body structure data, which for example can be a model of a bone surface, in order to use the body structure data together with the position data during a navigation-assisted operation and/or when planning operative incisions. Such a navigation system is for example described in DE 196 39 615 C2.
In the following, segmenting an image data set is to be understood to mean that at least one region of the image data set is assigned to at least one body structure. For example, image regions can be determined which depict individual vertebra of a complete spine. In this respect, segmentation facilitates the interpretation and comprehension of available image data.
Segmenting two-dimensional image data sets and also three-dimensional image data sets (volume data sets) is known in the prior art. Such image data sets can be generated using imaging methods which are common in the medical field. A two-dimensional image data set is for example generated in a conventional x-ray recording or an ultrasound recording. Three-dimensional image data sets are for example generated in a computer tomograph or in a recording by means of a nuclear spin tomograph. Where these image data sets are to be segmented, segmenting three-dimensional image data sets has proven particularly complicated, as compared to segmenting two-dimensional image data sets. The methods for segmenting two-dimensional image data sets are relatively well developed in the non-medical sector in particular, for example in face recognition methods.
In the field of segmenting two-dimensional image data sets, so-called snake methods or also balloon methods are used, which are model-based methods in which closed paths are defined around or within the region to be assigned (the body structure). Preferably using iterative methods, said paths are approximated to the actual two-dimensional structure of the region to be identified, by using snakes from outside inwards and balloons from inside outwards.
Attempts have been made to use the known segmenting methods for two-dimensional image data sets, outlined above, for three-dimensional image data sets as well, wherein the three-dimensional image data sets were broken down into a number of two-dimensional image data sets, and segmentation for the two-dimensional image data sets was individually performed in each case. However, this is problematic, since the relations between the individual layers of the two-dimensional representation can be lost. The transition between the individual layers is unsatisfactory, and the results of segmentation for each individual layer are often not consistent with the results for the preceding layer.
Attempts have also been made to segment three-dimensional image data sets directly. For this purpose, for example, an atlas-based method was used which uses a pre-segmented volume, the so-called atlas, wherein segmentation is already performed once beforehand on the basis of a model which correctly represents a body structure for most patients, and an attempt is made to perform segmentation by fitting said model to the actually available patient data. However, this is a computationally very time-consuming process.